Machine Learning Resident
3 months ago
Atefeh Shahroudnejad, Machine Learning Scientist and Mara Cairo, Product Owner, Advanced Technology
**About the Role**
This is a paid Residency that will be undertaken over a twelve-month period with the potential to be hired by our client, Amplifier Health, afterwards (note: at the discretion of the client). The Resident will report to an Amii Scientist and regularly consult and work with our partners to share insights and engage in knowledge transfer activities.
**About the Client**
Amplifier Health is pioneering a new frontier in healthcare diagnostics by harnessing the power of voice. We're developing the world's first foundational Large Acoustic Model (LAM), a cutting-edge AI technology that analyzes biomarkers within patients' voices to detect and monitor a wide range of health conditions.
Our platform has the potential to revolutionize healthcare by providing a non-invasive, cost-effective, and scalable solution for early disease detection and personalized treatment. Imagine a world where a simple phone call could reveal early signs of Parkinson's, monitor the progression of dementia, or even predict future health risks. That's the future Amplifier Health is building.
We're a team of passionate innovators, driven by the belief that voice can unlock a wealth of health data that has remained untapped until now. We're backed by leading investors and are collaborating with top research institutions to validate our technology and bring it to market.
At Amplifier Health, we're not just building a product; we're creating a new category of healthcare solutions. We're looking for exceptional AI researchers to join us on this journey and help shape the future of voice-based diagnostics. If you're excited about the potential of AI to transform healthcare and want to work on groundbreaking technology with a real-world impact, we encourage you to apply.
**About the Project**
This is a project that offers a unique opportunity to be at the forefront of AI-driven healthcare innovation. You will be an integral part of the team that is building the world's first foundation Large Acoustic Model (LAM) for voice-based diagnostics.
The project will involve three key phases:
- **Validation and Critical Assessment**: You will start by rigorously evaluating our existing methodology and technology stack. We encourage you to challenge assumptions, identify potential weaknesses, and propose improvements to enhance the robustness and reliability of our platform.
- **Refinement and Enhancement**: Based on your findings, you will implement solutions to address any identified gaps or limitations. This may involve refining algorithms, optimizing data pipelines, or developing new features to strengthen the platform's capabilities.
- **Condition-Specific Experimentation and Fine-Tuning**: The core of the project will focus on conducting in-depth experiments on specific health conditions. You will work on fine-tuning the LAM to improve its performance in detecting and monitoring conditions of commercial interest. This will involve collecting and analyzing data, developing and testing new models, and iteratively refining the platform to achieve optimal accuracy and sensitivity.
By the end of this project, you will have made significant contributions to the development of a groundbreaking technology that has the potential to transform healthcare. You will have gained invaluable experience working on a cutting-edge AI platform and will have played a crucial role in bringing it closer to commercialization.
**Required Skills / Expertise**
Are you passionate about building great solutions? You’ll be presented with opportunities to both personally and professionally develop as you build your career. We’re looking for a talented and enthusiastic individual with a solid background in machine learning, speech/voice processing, and Large Language Models (LLMs).
**Key Responsibilities**:
- Utilize a range of ML and speech processing approaches to identify biomarkers and detect health conditions in a short voice sample, and generate synthetic speech
- Leverage LLMs to inject non-acoustic information into the models
- Collaborate with the project team and Indigenous domain experts to iteratively curate training data
- Participate in regular meetings with the client and stakeholders, preparing presentations and reports
- Research and implement different approaches to the problem from literature as well as publicly available tools. Perform exploratory data analysis and statistical analysis to understand the nuance of the data as well as model performance.
**Required Qualifications**:
- Familiarity with and hands-on experience in acoustic models and digital biomarkers
- Familiarity with and hands-on experience with the state-of-the-art open-source LLMs (e.g., through HuggingFace)
- Experience using speech and audio processing tools and techniques
- Proficient in developing and training, fine-tuning and evaluating deep neural netwo
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